
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import requests


df = pd.read_csv('Meteorite_Landings.csv', index_col=0)
df = df.dropna() # dropping all null rows


# Lets break up year column into just years
y = list()

for index, row in df.iterrows():
  y.append(row['year'][6:10])

df['years'] = y
df = df[df.years.astype(int) > 1900]

df["years"] = pd.to_numeric(df["years"])

df.head()


# for the map description, lets add a description column
df['Description'] = "Mass: " + df['mass (g)'].astype(str) + ", Name: " + df.index.values.astype(str)
df


df_fell = df[(df['fall'] == 'Fell')]
df_fell


import matplotlib
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 20, 'figure.figsize': (15, 8)}) 

# for coordinates
import requests
from shapely.geometry import mapping, shape
from shapely.prepared import prep
from shapely.geometry import Point


# reverse-geo locating coordinates to countries
# using github which was faster than google api
geoData = requests.get("https://raw.githubusercontent.com/datasets/geo-countries/master/data/countries.geojson").json()
country_list = list()

geo = df["GeoLocation"].values.tolist()
lat = df["reclat"].values.tolist()
lon = df["reclong"].values.tolist()

# stripping and cleaning up data coordinates
geo = [i.strip('()') for i in geo]
geo = [i.replace(' ', "") for i in geo]
geo = [i.replace(',', " ") for i in geo]
geo = [i.split(' ') for i in geo]
for i in range(len(geo)):
  geo[i][0] = float(geo[i][0])
  geo[i][1] = float(geo[i][1])

strtemp = " ".join([str(i) for i in geo])
geo = strtemp.replace("[", "").replace("]", "").replace(",", " ").replace("  ", " ").split(" ")

countries = {}
for i in geoData["features"]:
    geom = i["geometry"]
    country = i["properties"]["ADMIN"]
    countries[country] = prep(shape(geom))

def get_country(lon, lat):
    point = Point(lon, lat)
    for country, geom in countries.items():
        if geom.contains(point):
            return country
    return "unknown"

print("(This might take a second)...searching coordinates...assigning country names...\n")

# make list of countries if coordinates are known
for i in range(len(lon)):
  if get_country(lat[i], lon[i]) != "unknown":
    country_list.append(get_country(lat[i], lon[i]))
  else:
    country_list.append("Unknown")
  
# adding a country column
df["country"] = country_list

# Displays country with most/least frequent landings

countries = list()

for i in country_list:
    if i not in countries and i != "Unknown":
        countries.append(i)
        
cc = [i for i in country_list if i != "Unknown"]
biggest_country = max(set(cc), key = cc.count) 
smallest_country = min(set(cc), key = cc.count) 

print("Country with the most frequent landings is:", biggest_country)
print("Country with the least frequent landings is:", smallest_country)

(This might take a second)...searching coordinates...assigning country names...

Country with the most frequent landings is: Sudan
Country with the least frequent landings is: Austria


# dropping unknown countries
df = df[df.country != "Unknown"]
df


# Displays bar chart to show country's with landings
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (29,26)

plt.hist(df['country'], bins=70, alpha=0.5, histtype='bar', ec='black');
plt.grid()
plt.yticks(np.arange(0, 1500, 30))

plt.xticks(
    rotation=45, 
    horizontalalignment = 'right',
    fontweight = 'light',
    fontsize = 16
)

plt.ylabel('Landing Frequency', size=20)
plt.xlabel('Countries', size=20)
plt.title('Meteor Landing Frequency per Recorded Country (1901-2013)', size=20)
plt.show()


cols = ['red', 'green', 'blue', 'orange', 'yellow', 'purple']


import folium

def mapping(df, cluster_column):
    m = folium.Map(location=[df.reclat.mean(), df.reclong.mean()], zoom_start=2)
    for index, row in df.iterrows():
      if row[cluster_column] == -1: # outlier
            cluster_colour = 'black'
      else:
            cluster_colour = cols[row[cluster_column]]
  
      folium.CircleMarker(
            location= [row['reclat'], row['reclong']],
            radius=3,
            popup= row['Description'],
            color=cluster_colour,
            fill=True,
            fill_color=cluster_colour
        ).add_to(m)
        
    return m


import geopandas
from sklearn.cluster import DBSCAN

# since we have date up to 2013 we can assume it will not be updated any tiem soon, so we will manually calculate the eps and minvalues by testing it
geocoords = df[['reclat', 'reclong']].values.tolist()
db = DBSCAN(eps=0.1, min_samples=30, algorithm='ball_tree', metric='haversine').fit(np.radians(geocoords))

class_predictions = db.labels_
df['db_labels'] = class_predictions
m = mapping(df, 'db_labels')

m

/tmp/ipykernel_2242/3611754964.py:9: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['db_labels'] = class_predictions

df


# https://sne.space/
# lets see if there were supernovas during the years of asteroids hitting earth
df2 = pd.read_csv('The Open Supernova Catalog (1).csv', index_col=0)
df2 = df2.dropna(subset=['Disc. Date']) # dropping all null rows that dont contain a date
df2 = df2.drop(['R.A.', 'Dec.', 'Type', 'Phot.', 'Spec.', 'Radio', 'X-ray', 'mmax'], axis=1) # stuff we dont care about

df2


# Lets break up year column into just years
year = list()

for index, row in df2.iterrows():
  year.append(row['Disc. Date'][:4])


from operator import contains
# getting rid of invalid years

df2['years'] = year
df2 = df2[df2.years.str.contains("^(19|20)\d{2}$")]

/tmp/ipykernel_2242/2107646420.py:5: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
  df2 = df2[df2.years.str.contains("^(19|20)\d{2}$")]


#let's drop years before 1490 and after 2013 to match years in df
df2["years"] = pd.to_numeric(df2["years"])
df2 = df2[df2.years < 2014]

df2.head()

/tmp/ipykernel_2242/871189985.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df2["years"] = pd.to_numeric(df2["years"])


# now lets look at overall meterotie landing fren
plt.xticks(
    rotation=45, 
    horizontalalignment = 'right',
    fontweight = 'light',
    fontsize = 13
)

plt.hist(df['years'], bins=70, alpha=0.5, histtype='bar', ec='black')
plt.show();


# now lets look at overall supernova landing frequency
df2 = df2.sort_values(by="years")

plt.xticks(
    rotation=45, 
    horizontalalignment = 'right',
    fontweight = 'light',
    fontsize = 13
)

plt.hist(df2['years'], bins=70, alpha=0.5, histtype='bar', ec='black')
plt.show();


df_1 = pd.DataFrame()
df_2 = pd.DataFrame()

df_1["supernovas"] = df2["years"].value_counts()
df_1 = df_1.reset_index()
df_1 = df_1.rename(columns={"index": "years"})
df_1 = df_1.sort_values(by="years")


df_2["meteorites"] = df["years"].value_counts()
df_2 = df_2.reset_index()
df_2 = df_2.rename(columns={"index": "years"})
df_2 = df_2.sort_values(by="years")

df_major = pd.merge(df_1, df_2, on=["years"])


df_major


# fitting to linear regression equation 
import statsmodels.api as sm
from statsmodels.formula.api import ols

fit = np.polyfit(df_major["supernovas"].astype(int), df_major["meteorites"],1)
x = fit[0]
intercept = fit[1]
y = x*(df_major["supernovas"]) + intercept

print('y = {:.2f} * supernovas + ({:.2f})'.format(x, intercept))

result = ols(formula = "meteorites ~ supernovas", data=df_major).fit()
print(result.summary())

y = 0.02 * supernovas + (37.96)
                            OLS Regression Results                            
==============================================================================
Dep. Variable:             meteorites   R-squared:                       0.017
Model:                            OLS   Adj. R-squared:                  0.005
Method:                 Least Squares   F-statistic:                     1.442
Date:                Mon, 20 Dec 2021   Prob (F-statistic):              0.233
Time:                        01:49:45   Log-Likelihood:                -501.32
No. Observations:                  86   AIC:                             1007.
Df Residuals:                      84   BIC:                             1012.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept     37.9595      9.777      3.882      0.000      18.516      57.403
supernovas     0.0191      0.016      1.201      0.233      -0.013       0.051
==============================================================================
Omnibus:                       59.433   Durbin-Watson:                   0.420
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              188.589
Skew:                           2.491   Prob(JB):                     1.12e-41
Kurtosis:                       8.274   Cond. No.                         669.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.


sns.set(rc = {'figure.figsize':(20,8)})
ax = sns.lmplot(x="supernovas", y="meteorites", data=df_major, height=10);

ax.fig.suptitle("Scatter Plot of Meteorite frequencies as Supernovas Occur");

	id	nametype	recclass	mass (g)	fall	year	reclat	reclong	GeoLocation	years
name
Aarhus	2	Valid	H6	720.0	Fell	01/01/1951 12:00:00 AM	56.18333	10.23333	(56.18333, 10.23333)	1951
Abee	6	Valid	EH4	107000.0	Fell	01/01/1952 12:00:00 AM	54.21667	-113.00000	(54.21667, -113.0)	1952
Acapulco	10	Valid	Acapulcoite	1914.0	Fell	01/01/1976 12:00:00 AM	16.88333	-99.90000	(16.88333, -99.9)	1976
Achiras	370	Valid	L6	780.0	Fell	01/01/1902 12:00:00 AM	-33.16667	-64.95000	(-33.16667, -64.95)	1902
Adhi Kot	379	Valid	EH4	4239.0	Fell	01/01/1919 12:00:00 AM	32.10000	71.80000	(32.1, 71.8)	1919

	id	nametype	recclass	mass (g)	fall	year	reclat	reclong	GeoLocation	years	Description
name
Aarhus	2	Valid	H6	720.0	Fell	01/01/1951 12:00:00 AM	56.18333	10.23333	(56.18333, 10.23333)	1951	Mass: 720.0, Name: Aarhus
Abee	6	Valid	EH4	107000.0	Fell	01/01/1952 12:00:00 AM	54.21667	-113.00000	(54.21667, -113.0)	1952	Mass: 107000.0, Name: Abee
Acapulco	10	Valid	Acapulcoite	1914.0	Fell	01/01/1976 12:00:00 AM	16.88333	-99.90000	(16.88333, -99.9)	1976	Mass: 1914.0, Name: Acapulco
Achiras	370	Valid	L6	780.0	Fell	01/01/1902 12:00:00 AM	-33.16667	-64.95000	(-33.16667, -64.95)	1902	Mass: 780.0, Name: Achiras
Adhi Kot	379	Valid	EH4	4239.0	Fell	01/01/1919 12:00:00 AM	32.10000	71.80000	(32.1, 71.8)	1919	Mass: 4239.0, Name: Adhi Kot
...	...	...	...	...	...	...	...	...	...	...	...
Zillah 002	31356	Valid	Eucrite	172.0	Found	01/01/1990 12:00:00 AM	29.03700	17.01850	(29.037, 17.0185)	1990	Mass: 172.0, Name: Zillah 002
Zinder	30409	Valid	Pallasite, ungrouped	46.0	Found	01/01/1999 12:00:00 AM	13.78333	8.96667	(13.78333, 8.96667)	1999	Mass: 46.0, Name: Zinder
Zlin	30410	Valid	H4	3.3	Found	01/01/1939 12:00:00 AM	49.25000	17.66667	(49.25, 17.66667)	1939	Mass: 3.3, Name: Zlin
Zubkovsky	31357	Valid	L6	2167.0	Found	01/01/2003 12:00:00 AM	49.78917	41.50460	(49.78917, 41.5046)	2003	Mass: 2167.0, Name: Zubkovsky
Zulu Queen	30414	Valid	L3.7	200.0	Found	01/01/1976 12:00:00 AM	33.98333	-115.68333	(33.98333, -115.68333)	1976	Mass: 200.0, Name: Zulu Queen

	id	nametype	recclass	mass (g)	fall	year	reclat	reclong	GeoLocation	years	Description
name
Aarhus	2	Valid	H6	720.0	Fell	01/01/1951 12:00:00 AM	56.18333	10.23333	(56.18333, 10.23333)	1951	Mass: 720.0, Name: Aarhus
Abee	6	Valid	EH4	107000.0	Fell	01/01/1952 12:00:00 AM	54.21667	-113.00000	(54.21667, -113.0)	1952	Mass: 107000.0, Name: Abee
Acapulco	10	Valid	Acapulcoite	1914.0	Fell	01/01/1976 12:00:00 AM	16.88333	-99.90000	(16.88333, -99.9)	1976	Mass: 1914.0, Name: Acapulco
Achiras	370	Valid	L6	780.0	Fell	01/01/1902 12:00:00 AM	-33.16667	-64.95000	(-33.16667, -64.95)	1902	Mass: 780.0, Name: Achiras
Adhi Kot	379	Valid	EH4	4239.0	Fell	01/01/1919 12:00:00 AM	32.10000	71.80000	(32.1, 71.8)	1919	Mass: 4239.0, Name: Adhi Kot
...	...	...	...	...	...	...	...	...	...	...	...
Zemaitkiemis	30399	Valid	L6	44100.0	Fell	01/01/1933 12:00:00 AM	55.30000	25.00000	(55.3, 25.0)	1933	Mass: 44100.0, Name: Zemaitkiemis
Zhaodong	30404	Valid	L4	42000.0	Fell	01/01/1984 12:00:00 AM	45.81667	125.91667	(45.81667, 125.91667)	1984	Mass: 42000.0, Name: Zhaodong
Zhovtnevyi	30407	Valid	H6	107000.0	Fell	01/01/1938 12:00:00 AM	47.58333	37.25000	(47.58333, 37.25)	1938	Mass: 107000.0, Name: Zhovtnevyi
Zhuanghe	30408	Valid	H5	2900.0	Fell	01/01/1976 12:00:00 AM	39.66667	122.98333	(39.66667, 122.98333)	1976	Mass: 2900.0, Name: Zhuanghe
Zvonkov	30415	Valid	H6	2568.0	Fell	01/01/1955 12:00:00 AM	50.20000	30.25000	(50.2, 30.25)	1955	Mass: 2568.0, Name: Zvonkov

	id	nametype	recclass	mass (g)	fall	year	reclat	reclong	GeoLocation	years	Description	country
name
Aioun el Atrouss	423	Valid	Diogenite-pm	1000.0	Fell	01/01/1974 12:00:00 AM	16.39806	-9.57028	(16.39806, -9.57028)	1974	Mass: 1000.0, Name: Aioun el Atrouss	Angola
Aïr	424	Valid	L6	24000.0	Fell	01/01/1925 12:00:00 AM	19.08333	8.38333	(19.08333, 8.38333)	1925	Mass: 24000.0, Name: Aïr	Central African Republic
Akwanga	432	Valid	H	3000.0	Fell	01/01/1959 12:00:00 AM	8.91667	8.43333	(8.91667, 8.43333)	1959	Mass: 3000.0, Name: Akwanga	Nigeria
Al Zarnkh	447	Valid	LL5	700.0	Fell	01/01/2001 12:00:00 AM	13.66033	28.96000	(13.66033, 28.96)	2001	Mass: 700.0, Name: Al Zarnkh	Libya
Alberta	454	Valid	L	625.0	Fell	01/01/1949 12:00:00 AM	2.00000	22.66667	(2.0, 22.66667)	1949	Mass: 625.0, Name: Alberta	Algeria
...	...	...	...	...	...	...	...	...	...	...	...	...
Zerhamra	30403	Valid	Iron, IIIAB-an	630000.0	Found	01/01/1967 12:00:00 AM	29.85861	-2.64500	(29.85861, -2.645)	1967	Mass: 630000.0, Name: Zerhamra	Rwanda
Zillah 001	31355	Valid	L6	1475.0	Found	01/01/1990 12:00:00 AM	29.03700	17.01850	(29.037, 17.0185)	1990	Mass: 1475.0, Name: Zillah 001	Sudan
Zillah 002	31356	Valid	Eucrite	172.0	Found	01/01/1990 12:00:00 AM	29.03700	17.01850	(29.037, 17.0185)	1990	Mass: 172.0, Name: Zillah 002	Sudan
Zinder	30409	Valid	Pallasite, ungrouped	46.0	Found	01/01/1999 12:00:00 AM	13.78333	8.96667	(13.78333, 8.96667)	1999	Mass: 46.0, Name: Zinder	Cameroon
Zlin	30410	Valid	H4	3.3	Found	01/01/1939 12:00:00 AM	49.25000	17.66667	(49.25, 17.66667)	1939	Mass: 3.3, Name: Zlin	Yemen

	id	nametype	recclass	mass (g)	fall	year	reclat	reclong	GeoLocation	years	Description	country	db_labels
name
Aioun el Atrouss	423	Valid	Diogenite-pm	1000.0	Fell	01/01/1974 12:00:00 AM	16.39806	-9.57028	(16.39806, -9.57028)	1974	Mass: 1000.0, Name: Aioun el Atrouss	Angola	-1
Aïr	424	Valid	L6	24000.0	Fell	01/01/1925 12:00:00 AM	19.08333	8.38333	(19.08333, 8.38333)	1925	Mass: 24000.0, Name: Aïr	Central African Republic	0
Akwanga	432	Valid	H	3000.0	Fell	01/01/1959 12:00:00 AM	8.91667	8.43333	(8.91667, 8.43333)	1959	Mass: 3000.0, Name: Akwanga	Nigeria	-1
Al Zarnkh	447	Valid	LL5	700.0	Fell	01/01/2001 12:00:00 AM	13.66033	28.96000	(13.66033, 28.96)	2001	Mass: 700.0, Name: Al Zarnkh	Libya	-1
Alberta	454	Valid	L	625.0	Fell	01/01/1949 12:00:00 AM	2.00000	22.66667	(2.0, 22.66667)	1949	Mass: 625.0, Name: Alberta	Algeria	-1
...	...	...	...	...	...	...	...	...	...	...	...	...	...
Zerhamra	30403	Valid	Iron, IIIAB-an	630000.0	Found	01/01/1967 12:00:00 AM	29.85861	-2.64500	(29.85861, -2.645)	1967	Mass: 630000.0, Name: Zerhamra	Rwanda	0
Zillah 001	31355	Valid	L6	1475.0	Found	01/01/1990 12:00:00 AM	29.03700	17.01850	(29.037, 17.0185)	1990	Mass: 1475.0, Name: Zillah 001	Sudan	0
Zillah 002	31356	Valid	Eucrite	172.0	Found	01/01/1990 12:00:00 AM	29.03700	17.01850	(29.037, 17.0185)	1990	Mass: 172.0, Name: Zillah 002	Sudan	0
Zinder	30409	Valid	Pallasite, ungrouped	46.0	Found	01/01/1999 12:00:00 AM	13.78333	8.96667	(13.78333, 8.96667)	1999	Mass: 46.0, Name: Zinder	Cameroon	0
Zlin	30410	Valid	H4	3.3	Found	01/01/1939 12:00:00 AM	49.25000	17.66667	(49.25, 17.66667)	1939	Mass: 3.3, Name: Zlin	Yemen	2

	Disc. Date	Host Name
Name
SN2011fe	2011/08/24	M101
SN1987A	1987/02/24	LMC
SN2003dh	2003/03/31	A104450+2131
SN2013dy	2013/07/10	NGC 7250
SN2013ej	2013/07/25	NGC 628
...	...	...
SN1985M	1985/06/16	A220830-4830
SN1988M	1988/04/07	NGC 4496B
SN386A	386/04/30	Milky Way
SN393A	393/02/27	Milky Way
SN837A	837/04/29	Milky Way

	years	supernovas	meteorites
0	1901	2	4
1	1907	1	5
2	1912	1	1
3	1914	1	5
4	1915	1	3
...	...	...	...
81	2009	882	74
82	2010	1985	63
83	2011	1734	35
84	2012	1691	4
85	2013	2486	1